Identification of novel NLRP3 inhibitors: a comprehensive approach using 2D-QSAR, molecular docking, molecular dynamics simulation and drug-likeness evaluation

This research, employing computational methodologies, aimed to discover potential inhibitors for the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), an intracellular sensor pivotal in inflammation and various disease processes. Despite NLRP3's critical role, there remains a research gap in the identification of novel inhibitors, making this study’s objective significant. Through statistical techniques such as principal component analysis (PCA) and K-means clustering, data refinement and division was conducted in this research, leading to a more targeted set of potential inhibitors. By employing stepwise and subset multiple linear regression, a two-dimensional quantitative structure–activity relationship (2D-QSAR) model was developed, revealing six essential molecular descriptors for inhibitory activity. The interpretation of these descriptors led to the proposition of five potential compounds. One of these proposed compounds demonstrated remarkable binding affinity through molecular docking studies, marking it as a promising inhibitor of NLRP3. Further verification of this compound’s potential was conducted via molecular dynamics simulations, affirming its stability and interactions within the protein–ligand system. Compliance with lipinski’s rule of five indicated the drug-like properties of the proposed compounds and their potential for oral bioavailability. This study not only underscores the power of computational techniques in drug discovery but also highlights a promising candidate for therapeutic intervention against NLRP3-mediated inflammatory conditions. The identified compounds, particularly the one with remarkable binding affinity, may pave the way for future pharmacological advancements in treating inflammation-related diseases.